Author/Editor		Ni, H; Kavcic, V; Zhu, T; Ekholm, S; Zhong, J
Title		Effects of number of diffusion gradient directions on derived diffusion tensor imaging indices in human brain
Type		članek
Source		AJNR Am J Neuroradiol
Vol. and No.		Letnik 27, št. 8
Publication year		2006
Volume		str. 1776-81
Language		eng
Abstract		Background and purpose: The effects of a number of diffusion-encoding gradient directions (NDGD) on diffusion tensor imaging (DTI) indices have been studied previously with theoretic analysis and numeric simulations. In this study, we made in vivo measurements in the human brain to compare different clinical scan protocols and to evaluate their effects on the calculated DTI indices. Methods: Fifteen healthy volunteers were scanned with a 1.5T MR scanner. Single-shot DTI images were acquired using 3 protocols different in NDGD and number of excitations (NEX) for each direction (NDGD/NEX = 6/10, 21/3, 31/2). Means and standard error of mean (SEM) were calculated and compared in 6 regions of interest (ROIs) for mean diffusivity (D), fractional anisotropy (FA), diffusion tensor eigenvalues (lambda(1), lambda(2), and lambda(3)), and correlation coefficients (r) of these indices among the 3 DTI protocols. Results: At the ROI level, no significant differences were found for the mean and SEM of D and FA among protocols (P > .05). The 6-NDGD protocol, however, yielded higher values for lambda(1) and lambda(2) and lower values for lambda(3) in most ROIs (P < .05) compared with the other protocols. At the voxel level, the correlation between the protocols r(21-31) were higher than r(6-21) and r(6-31) in most ROIs. The correlation of FA among 3 protocols also increased with increasing anisotropy. Conclusion: For ROI analyses, different NDGDs lead to similar values of FA and D but different eigenvalues. However, different NDGDs at the voxel level provide varying values. The selection of the NDGD, therefore, should depend on the focus of different DTI applications.
Descriptors		ADULT ANISOTROPY ARTIFACTS BRAIN COMPUTER SIMULATION IMAGE ENHANCEMENT IMAGE PROCESSING, COMPUTER-ASSISTED REFERENCE VALUES SENSITIVITY AND SPECIFICITY